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Erschienen in: Soft Computing 4/2020

02.11.2018 | Focus

Uncertain Gompertz regression model with imprecise observations

verfasst von: Zeyu Hu, Jinwu Gao

Erschienen in: Soft Computing | Ausgabe 4/2020

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Abstract

Regression is widely applied in many fields. Regardless of the types of regression, we often assume that the observations are precise. However, in real-life circumstances, this assumption can only be met sometimes, which means the traditional regression methods can result in significant imprecise or biased predictions. Consequently, uncertain regression models might provide more accurate and meaningful results under these circumstances. In this article, we provide the residual analysis of uncertain Gompertz regression model, as well as the corresponding forecast value and confidence interval. Finally, we give a numerical example of uncertain Gompertz regression model.

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Metadaten
Titel
Uncertain Gompertz regression model with imprecise observations
verfasst von
Zeyu Hu
Jinwu Gao
Publikationsdatum
02.11.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 4/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-018-3611-1

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